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There exists an increasing need to understand the impact of climate change on the hydrological extremes of flood and drought, collectively referred to as 'hydro-hazards'. At present, current methodology are limited in their scope, particularly with respect to inadequate representation of the uncertainty in the hydroclimatological modelling chain.

This paper proposes spatially consistent comprehensive impact and uncertainty methodological framework for the identification of compound hydro-hazard hotspots - hotspots of change where concurrent increase in mean annual flood and drought events is projected. We apply a quasi-ergodic analysis of variance (QE-ANOVA) framework, to detail both the magnitude and the sources of uncertainty in the modelling chain for the mean projected mean change signal whilst accounting for non-stationarity. The framework is designed for application across a wide geographical range and is thus readily transferable. We illustrate the ability of the framework through application to 239 UK catchments based on hydroclimatological projections from the EDgE project (5 CMI5-GCMs and 3 HMs, forced under RCP8.5).

The results indicate that half of the projected hotspots are temporally concurrent or temporally successive within the year, exacerbating potential impacts on society. The north-east of Scotland and south-west of the UK were identified as spatio-temporally compound hotspot regions and are of particular concern. This intensification of the hydrologic dynamic (timing and seasonality of hydro-hazards) over a limited time frame represents a major challenge for future water management.

Hydrological models were identified as the largest source of variability, in some instances exceeding 80% of the total variance. Critically, clear spatial variability in the sources of modelling uncertainty was also observed; highlighting the need to apply a spatially consistent methodology, such as that presented. This application raises important questions regarding the spatial variability of hydroclimatological modelling uncertainty. In terms of water management planning, such findings allow for more focussed studies with a view to improving the projections which inform the adaptation process.